Total structural risk model

ABSTRACT

The present invention generally relates to financial data processing, and in particular it relates to credit scoring, consumer profiling, consumer behavior analysis and modeling. More specifically, it relates to risk modeling using the inputs of credit bureau data, size of wallet data, and, optionally, internal data.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of, and claims the benefit of, U.S.Ser. No. 12/938,247, filed Nov. 2, 2010, and entitled “TOTAL STRUCTURALRISK MODEL.” The '247 application is a continuation of, and claims thebenefit of, U.S. Pat. No. 7,849,004, issued on Dec. 7, 2010, andentitled “TOTAL STRUCTURAL RISK MODEL” (fka U.S. patent application Ser.No. 12/040,757, filed Feb. 29, 2008). All of which are herebyincorporated by reference in their entirety.

FIELD OF INVENTION

The present invention generally relates to financial data processing,and more particularly, to a system and method for credit scoring,consumer profiling, consumer behavior analysis and modeling.

BACKGROUND OF THE INVENTION

It is axiomatic that consumers will tend to spend more when they havegreater purchasing power. The capability to accurately estimate aconsumer's risk of default may allow a financial institution (such as acredit company, lender or any consumer services companies) to bettertarget potential prospects and identify any opportunities to increaseconsumer transaction volumes, without an undue increase in the risk ofdefaults. Attracting additional consumer spending, in turn, would oftenincrease such financial institution's revenues, primarily in the form ofan increase in transaction fees and interest payments received.Consequently, a consumer model that can accurately estimate risk ofdefault is of paramount interest to many financial institutions andother consumer services companies.

Sufficient systems are not available for appropriately estimating aconsumer's risk of default. The existing system typically includeslimited and incomplete consumer information from credit bureaus and thelike at the aggregate and individual consumer levels. In order toachieve a more complete picture of a consumer's risk of default, onemust examine in detail a larger range of a consumer's financialaccounts, including credit accounts, checking and savings accounts,investment portfolios, and the like. However, the vast majority ofconsumers do not maintain all such accounts with the same financialinstitution and access to detailed financial information from otherfinancial institutions is restricted by consumer privacy laws,disclosure policies and security concerns.

Accordingly, there is a need for a system and method for suitablymodeling a consumer's risk of default which addresses certain problemsof existing technologies.

SUMMARY OF THE INVENTION

The invention includes a method for determining a comprehensive consumerdefault risk value for a consumer. An exemplary method comprisesobtaining consumer credit data relating to the consumer, modelingconsumer spending patterns of the consumer using the consumer creditdata to obtain an estimated spend capacity of the consumer, andcalculating the comprehensive consumer default risk value for theconsumer based upon the consumer credit data and the estimated spendcapacity. The method may optionally include obtaining internal datarelating to the consumer and further calculating comprehensive consumerdefault risk value for the consumer based upon said internal data.

In various embodiments, the invention includes determining acomprehensive mortgage consumer default risk value for a consumer. Anexemplary method includes obtaining consumer credit data relating to theconsumer, modeling consumer spending patterns of the consumer using theconsumer credit data to obtain an estimated spend capacity of theconsumer, and calculating the comprehensive consumer default risk valuefor the consumer based upon the consumer credit data and the estimatedspend capacity. The method may optionally include obtaining internaldata relating to the consumer and further calculating comprehensiveconsumer default risk value for the consumer based upon said internaldata.

The present invention may also allow an issuer to create a risk modelfor use in targeting potential consumers, make credit decisionsregarding existing consumers, and increase business with businesspartners.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of exemplary categories of consumers, in accordancewith an exemplary embodiment of the present invention.

FIG. 2 is a diagram of exemplary subcategories of consumers, inaccordance with an exemplary embodiment of the present invention.

FIG. 3 is a diagram of exemplary financial data used for modelgeneration and validation, in accordance with an exemplary embodiment ofthe present invention.

FIG. 4 is a flowchart of an exemplary process for estimating the spendability of a consumer, in accordance with an exemplary embodiment of thepresent invention.

FIG. 5 is a flowchart of an exemplary process for modeling consumerdefault risk.

FIG. 6 is a flowchart of an exemplary process for calculating acomprehensive consumer default risk value.

DETAILED DESCRIPTION

The detailed description herein is presented for purposes ofillustration only and not of limitation. For example, the steps recitedin any of the method or process descriptions may be executed in anyorder and are not limited to the order presented. For the sake ofbrevity, conventional data networking, application development and otherfunctional aspects of the systems (and components of the individualoperating components of the systems) may not be described in detailherein. Although the present invention is described as relating to riskmodeling of individual consumers, one of skill in the pertinent artswill recognize that the various embodiments of the invention can alsoapply to small businesses and organizations without departing from thespirit and scope of the present invention.

Modeling Consumer Risk

Modeling consumer risk includes, in one embodiment, obtaining consumerdata, modeling and/or processing the consumer data, and creating anoutput. The output may then be used to make business decisions. Invarious embodiments, the present invention uses a variety of data (e.g.,consumer data) in conjunction with several modeling/processingprocedures to assess risk.

A debt obligation includes any obligation a consumer has to pay a lendermoney. Any extension of credit from a lender to a consumer is alsoconsidered a debt obligation. A debt obligation may be secured orunsecured. Secured obligations may be secured with either real orpersonal property. A loan or a credit account are types of debtobligations. A security backed by debt obligations is considered a debtobligation itself. A mortgage includes a loan, typically in the form ofa promissory note, secured by real property. The real property may besecured by any legal means, such as, for example, via a mortgage or deedof trust. For convenience, a mortgage is used herein to refer to a loansecured by real property. An automobile loan includes a loan, typicallyin the form of a promissory note, which is secured by an automobile. Forconvenience, an automobile loan is used herein to refer to a loansecured by an automobile.

A lender is any person, entity, software and/or hardware that provideslending services. A lender may deal in secured or unsecured debtobligations. A lender may engage in secured debt obligations whereeither real or personal property acts as collateral. A lender need notoriginate loans but may hold securities backed by debt obligations. Alender may be only a subunit or subdivision of a larger organization. Amortgage holder includes any person or entity that is entitled torepayment of a mortgage. An automobile loan holder is any person orentity that is entitled to repayment of an automobile loan. As usedherein, the terms lender and credit issuer may be used interchangeably.Credit issuers may include financial services companies that issuecredit to consumers.

A trade or tradeline includes a credit or charge vehicle typicallyissued to an individual consumer by a credit grantor. Types oftradelines include, for example, bank loans, credit card accounts,retail cards, personal lines of credit and car loans/leases.

Tradeline data describes the consumer's account status and activity suchas, for example, names of companies where the consumer has accounts,dates such accounts were opened, credit limits, types of accounts,balances over a period of time and summary payment histories. Tradelinedata is generally available for the vast majority of actual consumers.Tradeline data, however, typically does not include individualtransaction data, which is largely unavailable because of consumerprivacy protections. Tradeline data may be used to determine bothindividual and aggregated consumer spending patterns, as describedherein.

Internal data is any data a credit issuer possesses or acquirespertaining to a particular consumer. Internal data may be gatheredbefore, during, or after a relationship between the credit issuer andthe consumer. Such data may include consumer demographic data. Consumerdemographic data includes any data pertaining to a consumer. Consumerdemographic data may include consumer name, address, telephone number,email address, employer and social security number. Consumertransactional data is any data pertaining to the particular transactionsin which a consumer engages during any given time period. Consumertransactional data may include transaction amount, transaction time,transaction vendor/merchant, and transaction vendor/merchant location.Transaction vendor/merchant location may contain a high degree ofspecificity to a vendor/merchant. For example, transactionvendor/merchant location may include a particular gasoline filingstation in a particular postal code located at a particular crosssection or address. Also for example, transaction vendor/merchantlocation may include a particular web address, such as a UniformResource Locator (“URL”), an email address and/or an Internet Protocol(“IP”) address for a vendor/merchant. Transaction vendor/merchantlocation may also include information gathered from a WHOIS databasepertaining to the registration of a particular web or IP address. WHOISdatabases include databases that contain data pertaining to Internet IPaddress registrations. Transaction vendor/merchant, and transactionvendor/merchant location may be associated with a particular consumerand further associated with sets of consumers. Consumer payment dataincludes any data pertaining to a consumer's history of paying debtobligations. Consumer payment data may include consumer payment dates,payment amounts, balance amount, and credit limit. Internal data mayfurther comprise records of consumer service calls, complaints, requestsfor credit line increases, questions, and comments. A record of aconsumer service call includes, for example, date of call, reason forcall, and any transcript or summary of the actual call.

Internal data may further comprise closed-loop data and open-loop data.Closed-loop data includes data obtained from a credit issuer'sclosed-loop transaction system. A closed-loop transaction systemincludes transaction systems under the control of one party. Closed-looptransaction systems may be used to obtain consumer transactional data.Open-loop data includes data obtained from a credit issuer's open-looptransaction system. An open-loop transaction system includes transactionsystems under the control of multiple parties. Credit bureau data is anydata retained by a credit bureau pertaining to a particular consumer. Acredit bureau is any organization that collects and/or distributesconsumer data. A credit bureau may be a consumer reporting agency.Credit bureaus generally collect financial information pertaining toconsumers. Credit bureau data may include consumer account data, creditlimits, balances, and payment history. Credit bureau data may includecredit bureau scores that reflect a consumer's creditworthiness. Creditbureau scores are developed from data available in a consumer's file,such as the amount of lines of credit, payment performance, balance, andnumber of tradelines. The data available in a consumer's file is used tomodel the risk of a consumer over a period of time using statisticalregression analysis. In one embodiment, those data elements that arefound to be indicative of risk are weighted and combined to determinethe credit score. For example, each data element may be given a score,with the final credit score being the sum of the data element scores.

Any transaction account or credit account discussed herein may includean account or an account number. An “account” or “account number”, asused herein, may include any device, code, number, letter, symbol,digital certificate, smart chip, digital signal, analog signal,biometric or other identifier/indicia suitably configured to allow theconsumer to access, interact with or communicate with the system (e.g.,one or more of an authorization/access code, personal identificationnumber (PIN), Internet code, other identification code, and/or thelike). The account number may optionally be located on or associatedwith a rewards card, charge card, credit card, debit card, prepaid card,telephone card, embossed card, smart card, magnetic stripe card, barcode card, transponder, radio frequency card or an associated account.The system may include or interface with any of the foregoing cards ordevices, or a fob having a transponder and RFID reader in RFcommunication with the fob. Although the system may include a fobembodiment, the invention is not to be so limited. Indeed, system mayinclude any device having a transponder which is configured tocommunicate with RFID reader via RF communication. Typical devices mayinclude, for example, a key ring, tag, card, cell phone, wristwatch orany such form capable of being presented for interrogation. Moreover,the system, computing unit or device discussed herein may include a“pervasive computing device,” which may include a traditionallynon-computerized device that is embedded with a computing unit. Examplescan include watches, Internet enabled kitchen appliances, restauranttables embedded with RF readers, wallets or purses with imbeddedtransponders, etc.

To model consumer spending power, consumer spend may be determined overprevious periods of time (sometimes referred to herein as the consumer'ssize of wallet) from tradeline data sources. The share of wallet bytradeline or account type may also be determined. The size of wallet(“SoW”) is represented by a consumer's or business' total aggregatespending and the share of wallet represents how the consumer usesdifferent payment instruments. Methods and apparatus for calculating thesize of wallet have been disclosed in U.S. patent application Ser. No.11/169,588 which was published with publication number 2006-0242046 A1,the disclosure of which is hereby incorporated by reference in itsentirety. Methods and apparatus for calculating the size of wallet havealso been disclosed in U.S. patent application Ser. No. 11/586,737 whichwas published with publication number US 2007-0226130 A1, the disclosureof which is hereby incorporated by reference in its entirety. Exemplarysize of wallet determinations will now be discussed in detail.

Consumer panel data measures consumer spending patterns from informationthat is provided by, typically, millions of participating consumerpanelists. Exemplary consumer panel data is available through variousconsumer research companies, such as comScore Networks, Inc. of Reston,Va. Consumer panel data may include individual consumer information suchas, for example, credit risk scores, credit card application data,credit card purchase transaction data, credit card statement views,tradeline types, balances, credit limits, purchases, balance transfers,cash advances, payments made, finance charges, annual percentage ratesand fees charged. Such individual information from consumer panel data,however, may be limited to those consumers who have participated in theconsumer panel, and so such detailed data may not be available for allconsumers. One skilled in the art will appreciate that the use of theterm “computer” or any similar term includes any type of hardware orsoftware in which a host is able to acquire information. Such computersmay include personal computers, personal digital assistants, biometricdevices, transaction account devices, loyalty accounts and/or the like.

As shown in FIG. 1, a population of consumers for which individualand/or aggregated data has been provided may be divided into two generalcategories for analysis, for example, those that are current on theircredit accounts (representing 1.72 million consumers in the exemplarydata sample size of 1.78 million consumers) and those that aredelinquent (representing 0.06 million of such consumers). In oneembodiment, delinquent consumers may be discarded from the populationsbeing modeled.

In further embodiments, the population of current consumers issubdivided into a plurality of further categories based on the amount ofbalance information available and the balance activity of such availabledata. In the example shown in FIG. 1, the amount of balance informationavailable is represented by a string of ‘+’ ‘0’ and ‘?’ characters. Eachcharacter represents one month of available data, with the rightmostcharacter representing the most current months and the leftmostcharacter representing the earliest month for which data is available.In the example provided in FIG. 1, a string of six characters isprovided, representing the six most recent months of data for eachcategory. The ‘+” character represents a month in which a credit accountbalance of the consumer has increased. The “0” character may representmonths where the account balance is zero. The “?” character representsmonths for which balance data is unavailable. Also provided in FIG. 1 isnumber of consumers that fall into each category and the percentage ofthe consumer population they represent in that sample.

In further embodiments, only certain categories of consumers may beselected for modeling behavior. The selection may be based on thosecategories that demonstrate increased spend on their credit balancesover time. However, it should be readily appreciated that othercategories can be used. FIG. 1 shows an example of two categories ofselected consumers for modeling (+++++, ???+++). These groups show theavailability of at least the three most recent months of balance dataand that the balances increased in each of those months.

Turning now to FIG. 2, which shows sub-categorization of the twocategories (+++++, ???+++) that are selected for modeling. In theembodiment shown, the sub-categories may include: consumers having amost recent credit balance less than $400; consumers having a mostrecent credit balance between $400 and $1600; consumers having a mostrecent credit balance between $1600 and $5000; consumers whose mostrecent credit balance is less than the balance of, for example, threemonths ago; consumers whose maximum credit balance increase over, forexample, the last twelve months divided by the second highest maximumbalance increase over the same period is less than 2; and consumerswhose maximum credit balance increase over the last twelve monthsdivided by the second highest maximum balance increase is greater than2. It should be readily appreciated that other subcategories can beused. Each of these subcategories is defined by their last month balancelevel. The number of consumers from the sample population (in millions)and the percentage of the population for each category are also shown inFIG. 2.

There may be a certain balance threshold established, wherein if aconsumer's account balance is too high, their behavior may not bemodeled, since such consumers are less likely to have sufficientspending ability. In another embodiment, consumers having balances abovesuch threshold may be sub-categorized yet again, rather than completelydiscarded from the sample. In the example shown in FIG. 2, the thresholdvalue may be $5000, and only those having particular historical balanceactivity may be selected, i.e. those consumers whose present balance isless than their balance three months earlier, or whose maximum balanceincrease in the examined period meets certain parameters. Otherthreshold values may also be used and may be dependent on the individualand aggregated consumer data provided.

The models generated may be derived, validated and refined usingtradeline and consumer panel data. An example of tradeline data 500 fromExperian and consumer panel data 502 from comScore is represented inFIG. 3. Each row of the data represents the record of one consumer andthousands of such records may be provided at a time. The statement showsthe point-in-time balance of consumers accounts for three successivemonths (Balance 1, Balance 2 and Balance 3). The data shows eachconsumer's purchase volume, last payment amount, previous balance amountand current balance. Such information may be obtained, for example, bypage scraping the data (in any of a variety of known manners usingappropriate application programming interfaces) from an Internet website or network address at which the data is displayed.

Furthermore, the data may be matched by consumer identity and combinedby one of the data providers or another third party independent of thefinancial institution. Validation of the models using the combined datamay then be performed, and such validation may be independent ofconsumer identity.

Turning now to FIG. 4, an exemplary process for estimating the size ofan individual consumer's spending wallet is shown. Upon completion ofthe modeling of the consumer categories above, the process commenceswith the selection of individual consumers or prospects to be examined(step 602). An appropriate model derived for each category will then beapplied to the presently available consumer trade line information inthe following manner to determine, based on the results of applicationof the derived models, an estimate of a consumer's size of wallet. Eachconsumer of interest may be selected based on their falling into one ofthe categories selected for modeling described above, or may be selectedusing any of a variety of criteria.

The process continues to step 604 where, for a selected consumer, apaydown percentage over a previous period of time is estimated for eachof the consumer's credit accounts. In one embodiment, the paydownpercentage is estimated over the previous three-month period of timebased on available tradeline data, and may be calculated according tothe following formula:

Pay-down %=(The sum of the last three months payments from theaccount)/(The (The sum of three month balances for the account based ontradeline data).

The paydown percentage may be set to, for example, 2%, for any consumerexhibiting less than a 5% paydown percentage, and may be set to 100% ifgreater than 80%, as a simplified manner for estimating consumerspending behaviors on either end of the paydown percentage scale.

Consumers that exhibit less than a 50% paydown during a three monthperiod may be categorized as revolvers, while consumers that exhibit a50% paydown or greater may be categorized as transactors. Thesecategorizations may be used to initially determine what, if any,purchasing incentives may be available to the consumer, as describedlater below.

The process then continues to step 606, where balance transfers for aprevious period of time are identified from the available tradeline datafor the consumer. Although tradeline data may reflect a higher balanceon a credit account over time, such higher balance may simply be theresult of a transfer of a balance into the account, and are thus notindicative of a true increase in the consumer's spending. It isdifficult to confirm balance transfers based on tradeline data since theinformation available is not provided on a transaction level basis. Inaddition, there are typically lags or absences of reporting of suchvalues on tradeline reports.

Nonetheless, marketplace analysis using confirmed consumer panel andinternal consumer financial records has revealed reliable ways in whichbalance transfers into an account may be identified from imperfectindividual tradeline data alone. Three exemplary reliable methods foridentifying balance transfers from credit accounts, each which is basedin part on actual consumer data sampled, are as follows.

It should be readily apparent that the formulas in the form recitedabove are not necessary for all embodiments of the present process andmay vary based on the consumer data used to derive them.

A first rule identifies a balance transfer for a given consumer's creditaccount as follows. The month having the largest balance increase in thetradeline data, and which satisfies the following conditions, may beidentified as a month in which a balance transfer has occurred:

-   -   The maximum balance increase is greater than twenty times the        second maximum balance increase for the remaining months of        available data;    -   The estimated pay-down percentage calculated at step 606 above        is less than 40%; and    -   The largest balance increase is greater than $1000 based on the        available data.

A second rule identifies a balance transfer for a given consumer'scredit account in any month where the balance is above twelve times theprevious month's balance and the next month's balance differs by no morethan 20%.

A third rule identifies a balance transfer for a given consumer's creditaccount in any month where:

-   -   the current balance is greater than 1.5 times the previous        month's balance;    -   the current balance minus the previous month's balance is        greater than $4500; and    -   the estimated pay-down percent from step 606 above is less than        30%.

The process then continues to step 608, where consumer spending on eachcredit account is estimated over the next, for example, three monthperiod. In estimating consumer spend, any spending for a month in whicha balance transfer has been identified from individual tradeline dataabove is set to zero for purposes of estimating the size of theconsumer's spending wallet, reflecting the supposition that no realspending has occurred on that account. The estimated spend for each ofthe three previous months may then be calculated as follows:

Estimated spend=(the current balance−the previous month's balance+(theprevious month's balance*the estimated pay-down % from step 604 above).

The exact form of the formula selected may be based on the category inwhich the consumer is identified from the model applied, and the formulais then computed iteratively for each of the three months of the firstperiod of consumer spend.

Next, at step 610, the estimated spend is then extended over, forexample, the previous three quarterly or three-month periods, providinga most-recent year of estimated spend for the consumer.

Finally, at step 612, the data output from step 610, in turn may be usedto generate a plurality of final outputs for each consumer account.These may be provided in an output file that may include a portion orall of the following exemplary information, based on the calculationsabove and information available from individual tradeline data:

(i) size of previous twelve month spending wallet; (ii) size of spendingwallet for each of the last four quarters; (iii) total number ofrevolving cards, revolving balance, and average pay down percentage foreach; (iv) total number of transacting cards, and transacting balancesfor each; (v) the number of balance transfers and total estimated amountthereof; (vi) maximum revolving balance amounts and associated creditlimits; and (vii) maximum transacting balance and associated creditlimit.

After step 612, the process may end with respect to the examinedconsumer. It should be readily appreciated that the process may berepeated for any number of current consumers or consumer prospects.

Such estimated spending may be calculated in a rolling manner acrosseach previous three month (quarterly) period. For example, spending ineach of a first three months of a first quarter may be calculated basedon balance values, the category of the consumer based on the abovereferenced consumer categorization spending models and the formulas usedin steps 604 and 606. Calculation may continue every three months, usingthe previous three months' data as an input.

It should be readily appreciated that as the rolling calculationsproceed, the consumer's category may change based on the outputs thatresult, and therefore, different formula corresponding to the newcategory may be applied to the consumer for different periods of time.The rolling manner described above maximizes the known data used forestimating consumer spend in a previous twelve month period. Based onthe final output generated for the consumer, commensurate purchasingincentives may be identified and provided to the consumer, for example,in anticipation of an increase in the consumer's purchasing ability asprojected by the output file. In such cases, consumers of good standing,who are categorized as transactors with a projected increase inpurchasing ability, may be offered a lower financing rate on purchasesmade during the period of expected increase in their purchasing ability,or may be offered a discount or rebate for transactions with selectedmerchants during that time.

It should be readily appreciated that as the rolling calculationsproceed, the consumer's category may change based on the outputs thatresult. Therefore, different formula corresponding to the new categorymay be applied to the consumer for different periods of time. Therolling manner described above maximizes the known data used forestimating consumer spend in a previous twelve month period Based on thefinal output generated for the consumer, commensurate purchasingincentives may be identified and provided to the consumer, for example,in anticipation of an increase in the consumer's purchasing ability asprojected by the output file. In such cases, consumers of good standing,who are categorized as transactors with a projected increase inpurchasing ability, may be offered a lower financing rate on purchasesmade during the period of expected increase in their purchasing ability,or may be offered a discount or rebate for transactions with selectedmerchants during that time.

In another example, and in the case where a consumer is a revolver, aconsumer with a projected increase in purchasing ability may be offereda lower annual percentage rate on balances maintained on their creditaccount. Other like promotions and enhancements to consumers'experiences are well known and may be used within the processesdisclosed herein.

Prospective consumer populations used for modeling and/or laterevaluation may be provided from any of a plurality of availablemarketing groups, or may be culled from credit bureau data, targetedadvertising campaigns or the like. Testing and analysis may becontinuously performed to identify the optimal placement and requiredfrequency of such sources for using the size of spending walletcalculations. The processes described herein may also be used to developmodels for predicting a size of wallet for an individual consumer in thefuture.

Institutions adopting the processes disclosed herein may expect to morereadily and profitably identify opportunities for prospect and consumerofferings, which in turn provides enhanced experiences across all partsof a consumer's lifecycle. In the case of a credit provider, accurateidentification of spend opportunities allows for rapid provisioning ofcard member offerings to increase spend that, in turn, results inincreased transaction fees, interest charges and the like. The carefulselection of consumers to receive such offerings reduces the incidenceof fraud that may occur in less disciplined cardmember incentiveprograms. The reduced incidence of fraud, in turn, reduces overalloperating expenses for institutions.

As mentioned above, the process described may also be used to developmodels for predicting a size of wallet for an individual consumer in thefuture. The capacity a consumer has for spending in a variety ofcategories is the share of wallet.

The model used to determine share of wallet for particular spendcategories using the processes described herein is the share of wallet(“SoW”) model. The SoW model provides estimated data and/orcharacteristics information that is more indicative of consumer spendingpower than typical credit bureau data or scores. The SoW model mayoutput, with sufficient accuracy, data that is directly related to thespend capacity of an individual consumer. One of skill in the art willrecognize that any one or combination of the following data types, aswell as other data types, may be output by the SoW model withoutaltering the spirit and scope of the present invention.

The size of a consumer's twelve-month spending wallet is an exampleoutput of the SoW model. A consumer's twelve-month spending wallet maybe output as an actual or rounded dollar amount. The size of aconsumer's spending wallet for each of several consecutive quarters, forexample, the most recent four quarters, may also be output.

The SoW model output may include the total number of revolving cardsheld by a consumer, the consumer's revolving balance, and/or theconsumer's average pay-down percentage of the revolving cards. Themaximum revolving balance and associated credit limits can be determinedfor the consumer, as well as the size of the consumer's revolvingspending.

Similarly, the SoW model output may include the total number of aconsumer's transaction cards and/or the consumer's transaction balance.The SoW model may additionally output the maximum transacting balance,the associated credit limit, and/or the size of transactional spendingof the consumer.

These outputs, as well as any other outputs from the SoW model, may beappended to data profiles of a company's consumers and prospects. Theoutput enhances the company's ability to make decisions involvingprospecting, new applicant evaluation, and consumer relationshipmanagement across the consumer lifecycle. The SoW score can focus, forexample, on total spend, transaction account spend and/or a consumer'sspending trend.

Using the processes described above, balance transfers are factored outof a consumer's spend capacity. Further, when correlated with a riskscore, the SoW score may provide more insight into behaviorcharacteristics of relatively low-risk consumers and relativelyhigh-risk consumers.

The SoW score may be structured in one of several ways. For instance,the score may be a numeric score that reflects a consumer's spend invarious ranges over a given time period, such as the last quarter oryear. As an example, a score of 5000 might indicate that a consumerspent between $5000 and $6000 in the given time period.

The score may include a range of numbers or a numeric indicator, such asan exponent, that indicates the trend of a consumer's spend over a giventime period. For example, a trend score of +4 may indicate that aconsumer's spend has increased over the previous 4 months, while a trendscore of −4 may indicate that a consumer's spend has decreased over theprevious 4 months.

In addition to determining an overall SoW score, the SoW model outputsmay each be given individual scores and used as attributes forconsideration in credit score development by, for example, traditionalcredit bureaus. As discussed above, credit scores are traditionallybased on information in a consumer's credit bureau file.

Outputs of the SoW model, such as balance transfer information, spendcapacity and trend, and revolving balance information, may be moreindicative of risk than some traditional data elements. Therefore, acompany may use scored SoW outputs in addition to or in place oftraditional data elements when computing a final credit score. SoWoutput information may be collected, analyzed, and/or summarized in ascorecard. Such a scorecard would be useful to, for example, creditbureaus, major credit grantors, and scoring companies, such as FairIsaac Corporation of Minneapolis, Minn.

The SoW modeling, however, is only one potential component of thecomprehensive consumer default risk that a consumer presents to alender. A comprehensive consumer default risk value is a value thatdescribes the risk that a consumer will default on any debt obligation.The debt obligation may be held by any lender or credit issuer.Calculating the comprehensive consumer default risk value can be done byany suitable means.

In various embodiments, the comprehensive consumer default risk value iscalculated using a SoW output combined with credit bureau data. Invarious embodiments, internal data may be used in addition to a SoWoutput and credit bureau data.

In various embodiments, calculating the comprehensive consumer defaultrisk value involves, as depicted in FIG. 5, obtaining consumer creditdata 701, modeling consumer spending patterns 702, and calculating acomprehensive consumer default risk value 703. Calculating thecomprehensive consumer default risk value may also involve obtaininginternal data for a given consumer 704.

Consumer credit data 701 may be obtained from any source such as, forexample, a credit bureau. Modeling consumer spending patterns mayinclude any process or method designed to assess the spending pattern ofa consumer such as, for example, the SoW model.

Calculating the comprehensive consumer default risk value 703 mayinvolve the process depicted in FIG. 6., namely, assigning a consumerpopulation segment 801, selecting an appropriate risk factorrelationship 802 and deriving a default probability 803 based upon saidrisk factor relationship. Assigning a consumer population segment 801includes any method for assigning consumers into population segments. Aconsumer population segment 801 may be based upon, for example, highrisk consumers and low risk consumers categories. A consumer populationsegment 801 may be based upon primary residence value. Selecting anappropriate risk factor relationship 802 may include any method ofcreating a relationship between risk factors. Selecting an appropriaterisk factor relationship 802 may be dependent upon the assigned consumerpopulation segment. Risk factors include any method of assessing risk.Risk factors may include risk factors derived from credit bureau data,internal data, merchant data, or any other factor that may be predictiveof risk. A risk factor relationship 802 may take the form of, forexample, an equation. An equation includes linear, exponential, andlogarithmic equations. An equation may assign fixed coefficientsassociated with a particular risk factor. A coefficient in an equationmay vary depending upon the particular consumer population segmentassigned. Deriving a default probability 803 based upon said risk factorrelationship 802 may take the form of, for example, an equation. Anequation includes linear, exponential, and logarithmic equations. Forexample, a logarithmic equation may transform a risk relationship into adefault probability 803. A default probability 803 may take the form ofa probability value between 0 and 1.

Applicable Market Segments/Industries

Outputs of the total structural risk (“TSR”) model can be used in anybusiness or market segment that extends credit or otherwise needs toevaluate the creditworthiness of a particular consumer. For simplicity,these businesses will be referred to herein as falling into one of threecategories: financial services companies, retail companies, and othercompanies.

The business cycle in each category may be divided into three phases:acquisition, retention, and disposal. The acquisition phase occurs whena business is attempting to gain new consumers. The acquisition phaseincludes, for example, targeted marketing, determining what products orservices to offer a consumer, deciding whether to lend to a particularconsumer and what the line size or loan should be, and deciding whetherto buy a particular loan. The retention phase occurs after a consumer isalready associated with the business. In the retention phase, thebusiness interests shift to managing the consumer relationship through,for example, consideration of risk, determination of credit lines,cross-sell opportunities, increasing business from that consumer, andincreasing the company's assets under management.

The disposal phase is entered when a business wishes to dissociateitself from a consumer or otherwise end the consumer relationship. Thedisposal phase can occur, for example, through settlement offers,collections, and sale of defaulted or near-default loans.

Financial services companies include, for example: banks and otherlenders, mutual fund companies, financiers of leases and sales, lifeinsurance companies, online brokerages, credit issuers, and loan buyers.

Banks and lenders can utilize the TSR model in all phases of thebusiness cycle. One exemplary use is in relation to home equity loansand the rating given to a particular bond issue in the capital market.The TSR model applies to home equity lines of credit and automobileloans in a similar manner.

If the holder of a home equity loan, for example, borrows from thecapital market, the loan holder issues asset-backed securities (“ABS”),or bonds, which are backed by receivables. The loan holder is thus anABS issuer. The ABS issuer applies for an ABS rating, which is assignedbased on the credit quality of the underlying receivables. One of skillin the art will recognize that the ABS issuer may apply for the ABSrating through any application means, without altering the spirit andscope of the present invention. In assigning a rating, the ratingagencies weigh a loan's probability of default by considering thelender's underwriting and portfolio management processes. Lendersgenerally secure higher ratings by credit enhancement. Examples ofcredit enhancement include, for example, over-collateralization, buyinginsurance (such as wrap insurance), and structuring ABS (through, forexample, senior/subordinate bond structures, sequential pay vs. parallelpay, etc.) to achieve higher ratings. Lenders and rating agencies takethe probability of default into consideration when determining theappropriate level of credit enhancement.

During the acquisition phase of a loan, lenders may use the TSR model toimprove their lending decisions. Before issuing the loan, lenders canevaluate a consumer's risk of default for making payments on the loan.Evaluation leads to fewer bad loans and a reduced probability of defaultfor loans in the lender's portfolio. A lower probability of defaultmeans that, for a given loan portfolio that was originated using the TSRmodel, either a higher rating can be obtained with the same degree ofover-collateralization, or the degree of over-collateralization can bereduced for a given debt rating. Thus, using the TSR model at theacquisition stage of the loan reduces the lender's overall borrowingcost and loan loss reserves.

During the retention phase of a loan, the TSR model can be used to tracka consumer's varying degree of risk. Based on the TSR outputs, thelender can make various decisions regarding the consumer relationship.For example, a lender may use the TSR model to identify borrowers whobecome more likely to default. The credit lines of those borrowers whichhave not fully been drawn down can then be reduced. Selectively revokingunused lines of credit may reduce the probability of default for loansin a given portfolio and reduce the lender's borrowing costs.Selectively revoking unused lines of credit may also reduce the lender'srisk by minimizing further exposure to a borrower that may already be infinancial distress.

Also during the retention phase of a loan, the TSR model may allowlenders to identify consumers for further marketing of businesspartners. A lender may partner with other businesses that wish to selltheir products and services to the lender's consumers. The lender canuse the TSR model to identify consumers with a risk of default profiledesired by its partners. The lender may then use that information toincrease those consumers' spend with the business partner. For example,a particular merchant may want to market to low risk, high spendconsumers. The lender may identify those consumers using the TSR modeland either convey the information to the particular partner merchantand, in one embodiment, market the particular merchant's goods to theconsumer directly.

During the disposal phase of a loan, the TSR model enables lenders tobetter predict the likelihood that a borrower will default. Once thelender has identified consumers who are in danger of default, the lendermay select those likely to repay and extend settlement offers.Additionally, lenders can use the TSR model to identify which consumersare unlikely to pay and those who are otherwise not worth extending asettlement offer.

The TSR model allows lenders to identify loans with risk of default,allowing lenders, prior to default, to begin anticipating a course ofaction to take if default occurs. Because freshly defaulted loans obtaina higher sale price than loans that have been non-performing for longertime periods, lenders may sell these loans earlier in the defaultperiod, thereby reducing the lender's costs.

The ability to predict and manage risk before default, often results ina lower likelihood of default for loans in the lender's portfolio.Further, even in the event of a defaulted loan, the lender can detectthe default early and thereby recoup a higher percentage of the value ofthat loan. A lender using the TSR model can thus show to the ratingagencies that it uses a combination of tight underwriting criteria androbust post-lending portfolio management processes. Tight underwritingcriteria and robust post-lending portfolio management processes enablethe lender to increase the ratings of the ABS that are backed by a givenpool or portfolio of loans and/or reduce the level ofover-collateralization or credit enhancement in order to obtain aparticular rating.

Turning to mutual funds, the TSR model may be used to manage therelationship with consumers who interact directly with the company.During the retention phase, if the mutual fund company concludes that aconsumer's risk of default has decreased, the company can then marketadditional funds to the consumer. The company can also cross-sell otherservices to the consumer with greater confidence.

Financiers of leases or sales, such as automobile lease or salefinanciers, can benefit from TSR outputs in much the same way as a bankor lender, as discussed above. In typical product financing, however,the amount of the loan or lease is based on the value of the productbeing financed. Therefore, there is generally no credit limit that needsto be revisited during the course of the loan. As there is no need torevisit credit limit, the TSR model is very useful to lease/salesfinance companies during the acquisition and disposal phases of thebusiness cycle.

Just as the TSR model can help loan holders determine that a particularloan is nearing default, loan buyers can use the model to evaluate thequality of a prospective purchase during the acquisition phase of thebusiness cycle. The TSR model thus may assist loan buyers in avoiding orreducing the sale prices of loans that are in likelihood of default.

Aspects of the retail industry for which the TSR model would beadvantageous include, for example: retail stores having private labelcards, on-line retailers, and mail order companies.

There are two general types of credit and charge cards in themarketplace today: multipurpose cards and private label cards. A thirdtype of hybrid card is emerging. Multipurpose cards are cards that canbe used at multiple different merchants and service providers. Forexample, American Express, Visa, Mastercard, and Discover are consideredmultipurpose card issuers. Multipurpose cards are accepted by merchantsand other service providers in what is often referred to as an “opennetwork.” Transactions are routed from a point-of-sale (“POS”) through anetwork for authorization, transaction posting, and settlement.

A variety of intermediaries play different roles in the process. Theseinclude merchant processors, the brand networks, and issuer processors.An open network is often referred to as an interchange network.Multipurpose cards include a range of different card types, such ascharge cards, revolving cards, and debit cards. Debit cards are linkedto a consumer's demand deposit account (“DDA”) or checking account.

Private label cards are cards that can be used for the purchase of goodsand services from a single merchant or service provider. Historically,major department stores were the originators of private label cards.Private label cards are now offered by a wide range of retailers andother service providers. These cards are generally processed on a closednetwork, with transactions flowing between the merchant's POS and itsown backoffice or the processing center for a third-party processor.These transactions do not flow through an interchange network and arenot subject to interchange fees.

Recently, a type of hybrid card has evolved. A hybrid card, when used ata particular merchant, is that merchant's private label card, but whenused elsewhere, becomes a multipurpose card. The particular merchant'stransactions are processed in the proprietary private label network.Transactions made with the card at all other merchants and serviceproviders are processed through an interchange network.

Private label card issuers, in addition to multipurpose card issuers andhybrid card issuers, can apply the TSR model in a similar way asdescribed above with respect to credit card companies. That is,knowledge of a consumer's risk of default, as well as knowledge of theother TSR outputs, may be used by card issuers to improve performanceand profitability across the entire business cycle.

Online retail and mail order companies can use the TSR model in both theacquisition and retention phases of the business cycle. During theacquisition phase, for example, the companies can base targetedmarketing strategies on TSR outputs. Use in the acquisition phase maysubstantially reduce costs, especially in the mail order industry, wherecatalogs are typically sent to a wide variety of individuals. During theretention phase, companies can, for example, base cross-sell strategiesor credit line extensions on TSR outputs.

Types of companies which also may make use of the TSR model include, forexample: the gaming industry, communications providers, and the travelindustry.

The gaming industry can use the TSR model in, for example, theacquisition and retention phases of the business cycle. Casinos oftenextend credit to their wealthiest and/or most active players, also knownas “high rollers.” The casinos can use the TSR model in the acquisitionphase to determine whether credit should be extended to an individual.Once credit has been extended, the casinos can use the TSR model toperiodically review the consumer's risk of default. If there is a changein the spend capacity, the casinos may alter the consumer's credit lineto be more commensurate with the consumer's risk of default.

Communications providers, such as telephone service providers, oftencontract into service plans with their consumers. In addition toimproving their targeted marketing strategies, communications providerscan use the TSR outputs during the acquisition phase to determine therisk of default on a service contract associated with a potentialconsumer.

Members of the travel industry can make use of the TSR outputs in theacquisition and retention stages of the business cycle. For example, ahotelier typically has a brand of hotel that is associated with aparticular “star-level” or class of hotel. In order to capture variousmarket segments, hoteliers may be associated with several hotel brandsthat are of different classes. During the acquisition phase of thebusiness cycle, a hotelier may use the TSR outputs to target individualsthat have appropriate spend capacities for various classes of hotels.During the retention phase, the hotelier may use the TSR outputs todetermine, for example, when a particular individual's risk of defaultdecreases. Based on that determination, the hotelier can market a higherclass of hotel to the consumer in an attempt to convince the consumer toupgrade.

One of skill in the relevant art(s) will recognize that many of theabove described TSR applications may be utilized by other industries andmarket segments without departing from the spirit and scope of thepresent invention. For example, the strategy of using TSR to model anindustry's “best consumer” and targeting individuals sharingcharacteristics of that best consumer can be applied to nearly allindustries.

TSR data can also be used across nearly all industries to improveconsumer loyalty by reducing the number of payment reminders sent toresponsible accounts. Responsible accounts are those that are mostlikely to pay even without being contacted by a collector. The reductionin reminders may increase consumer loyalty, because the consumer willnot feel that the lender or service provider is unduly aggressive. Thelender's or service provider's collection costs are also reduced, andresources are freed to dedicate to accounts requiring more persuasion.

Additionally, the TSR model may be used in any company having a largeconsumer service call center to identify specific types of consumers.Transcripts are typically made for any call from a consumer to a callcenter. These transcripts may be scanned for specific keywords ortopics, and combined with the TSR model to determine the consumer'scharacteristics. For example, a bank having a large consumer servicecenter may scan service calls for discussions involving bankruptcy. Thebank may then use the TSR model with the indications from the callcenter transcripts to evaluate the consumer.

The risk model may provide information to supplement a promotionalcampaign. Campaign statistics enable a merchant to view informationregarding any number of active and/or inactive offers for loan products.Such information may include, for example, campaign type, currentstatus, number of impressions, a number of clicks resulting from theimpressions, yield rate, average cost per yield, total cost, and thelike. Campaign statistics provide the merchant with a powerful tool thatmay lead to, for example, canceling or modifying a campaign if it is notprofitable. The campaign statistics interface further allows themerchant to pause and resume offer campaigns, edit campaigns, and deletecampaigns.

The present invention contemplates uses in association with webservices, utility computing, pervasive and individualized computing,security and identity solutions, autonomic computing, commoditycomputing, mobility and wireless solutions, open source, biometrics,grid computing and/or mesh computing.

A user may include any individual, business, entity, governmentorganization, software and/or hardware that requests a risk assessmentof a consumer. The user may interact with the system directly and mayview customized search results. A web client comprises any hardwareand/or software suitably configured to facilitate input, receipt and/orreview of information relating to merchants that are selected based on asearch term entered into a search engine such as, for example, Google™,Yahoo™, MSN™, AOL™, and/or any other Internet-wide or web site centricsearch engines. A web client includes any device (e.g., personalcomputer) which communicates (in any manner discussed herein) via anynetwork discussed herein. Such browser applications comprise Internetbrowsing software installed within a computing unit or system to conductonline transactions and/or communications. These computing units orsystems may take the form of a computer or set of computers, althoughother types of computing units or systems may be used, includinglaptops, notebooks, hand held computers, personal digital assistants,set-top boxes, workstations, computer-servers, main frame computers,mini-computers, PC servers, pervasive computers, network sets ofcomputers, and/or the like. Practitioners will appreciate that a webclient may or may not be in direct contact with an application server.For example, a web client may access the services of an applicationserver through another server, which may have a direct or indirectconnection to an Internet server.

As those skilled in the art will appreciate, a web client includes anoperating system (e.g., Windows NT, 95/98/2000, OS2, UNIX, Linux,Solaris, MacOS, etc.) as well as various conventional support softwareand drivers typically associated with computers. A web client mayinclude any suitable personal computer, network computer, workstation,minicomputer, mainframe or the like. A web client can be in a home orbusiness environment with access to a network. In an exemplaryembodiment, access is through a network or the Internet through acommercially available web-browser software package.

A web client may be independently, separately or collectively suitablycoupled to the network via data links which includes, for example, aconnection to an Internet Service Provider (ISP) over the local loop asis typically used in connection with standard modem communication, cablemodem, Dish networks, ISDN, Digital Subscriber Line (DSL), or variouswireless communication methods, see, e.g., GILBERT HELD, UNDERSTANDINGDATA COMMUNICATIONS (1996), which is hereby incorporated by reference.It is noted that the network may be implemented as other types ofnetworks, such as an interactive television (ITV) network.

A web client may include any number of applications, code modules,cookies, and the like to facilitate the permissive search functionalityas disclosed herein. In one embodiment, a web client includes apermissive search plug-in that is downloaded from an Internet serverprior to performing a search. A permissive search plug-in may includeany hardware and/or software suitably configured to detect when text isentered into a search box within a search interface and to submit theentered search text the application server for processing. In oneembodiment, a permissive search plug-in retrieves and stores informationrelating to a user within a memory structure of a web client in the formof a browser cookie, for example. In another embodiment, permissivesearch plug-in retrieves information relating to user from anapplication server.

A firewall, as used herein, may comprise any hardware and/or softwaresuitably configured to protect application server components from usersof other networks. A firewall may reside in varying configurationsincluding stateful inspection, proxy based and packet filtering amongothers. A Firewall may be integrated as software within an Internetserver, any other application server components or may reside withinanother computing device or may take the form of a standalone hardwarecomponent.

An Internet server may include any hardware and/or software suitablyconfigured to facilitate communications between a web client and one ormore application server components. Further, an Internet server may beconfigured to transmit data to a web client within markup languagedocuments. As used herein, “data” may include encompassing informationsuch as commands, queries, files, data for storage, and/or the like indigital or any other form. An Internet server may operate as a singleentity in a single geographic location or as separate computingcomponents located together or in separate geographic locations.

An Internet server may provide a suitable web site or otherInternet-based graphical user interface which is accessible by users. Inone embodiment, the Microsoft Internet Information Server (IIS),Microsoft Transaction Server (MTS), and Microsoft SQL Server, are usedin conjunction with the Microsoft operating system, Microsoft NT webserver software, a Microsoft SQL Server database system, and a MicrosoftCommerce Server. Additionally, components such as Access or MicrosoftSQL Server, Oracle, Sybase, Informix MySQL, InterBase, etc., may be usedto provide an Active Data Object (ADO) compliant database managementsystem.

Any of the communications, inputs, storage, databases or displaysdiscussed herein may be facilitated through a web site having web pages.The term “web page” as it is used herein is not meant to limit the typeof documents and applications that might be used to interact with theuser. For example, a typical web site might include, in addition tostandard HTML documents, various forms, Java applets, JavaScript, activeserver pages (ASP), common gateway interface scripts (CGI), extensiblemarkup language (XML), dynamic HTML, cascading style sheets (CSS),helper applications, plug-ins, and/or the like. A server may include aweb service that receives a request from a web server, the requestincluding a URL (http://yahoo.com/stockquotes/ge) and an IP address(123.56.789). The web server retrieves the appropriate web pages andsends the data or applications for the web pages to the IP address. Webservices are applications that are capable of interacting with otherapplications over a communications means, such as the Internet. Webservices are typically based on standards or protocols such as XML,SOAP, WSDL and UDDI. Web services methods are well known in the art, andare covered in many standard texts. See, e.g., ALEX NGHIEM, IT WEBSERVICES: A ROADMAP FOR THE ENTERPRISE (2003), hereby incorporated byreference.

Middleware may include any hardware and/or software suitably configuredto facilitate communications and/or process transactions betweendisparate computing systems. Middleware components are commerciallyavailable and known in the art. Middleware may be implemented throughcommercially available hardware and/or software, through custom hardwareand/or software components, or through a combination thereof. Middlewaremay reside in a variety of configurations and may exist as a standalonesystem or may be a software component residing on the Internet server.Middleware may be configured to process transactions between the variouscomponents of an application server and any number of internal orexternal issuer systems for any of the purposes disclosed herein.

A user database may include any hardware and/or software suitablyconfigured to facilitate storing identification, authenticationcredentials, user permissions, and user preferences. An Ad databasestores data relating to merchants and merchant incentive programs. Oneskilled in the art will appreciate that the system may employ any numberof databases in any number of configurations. Further, any databasesdiscussed herein may be any type of database, such as relational,hierarchical, graphical, object-oriented, and/or other databaseconfigurations. Common database products that may be used to implementthe databases include DB2 by IBM (White Plains, N.Y.), various databaseproducts available from Oracle Corporation (Redwood Shores, Calif.),Microsoft Access or Microsoft SQL Server by Microsoft Corporation(Redmond, Wash.), or any other suitable database product. Moreover, thedatabases may be organized in any suitable manner, for example, as datatables or lookup tables. Each record may be a single file, a series offiles, a linked series of data fields or any other data structure.Association of certain data may be accomplished through any desired dataassociation technique such as those known or practiced in the art. Forexample, the association may be accomplished either manually orautomatically. Automatic association techniques may include, forexample, a database search, a database merge, GREP, AGREP, SQL, using akey field in the tables to speed searches, sequential searches throughall the tables and files, sorting records in the file according to aknown order to simplify lookup, and/or the like. The association stepmay be accomplished by a database merge function, for example, using a“key field” in pre-selected databases or data sectors.

In one exemplary embodiment, the ability to store a wide variety ofinformation in different formats is facilitated by storing theinformation as a BLOB. Thus, any binary information can be stored in astorage space associated with a data set. As discussed above, the binaryinformation may be stored on the financial transaction instrument orexternal to but affiliated with the financial transaction instrument.The BLOB method may store data sets as ungrouped data elements formattedas a block of binary via a fixed memory offset using either fixedstorage allocation, circular queue techniques, or best practices withrespect to memory management (e.g., paged memory, least recently used,etc.). By using BLOB methods, the ability to store various data setsthat have different formats facilitates the storage of data associatedwith the system by multiple and unrelated owners of the data sets. Forexample, a first data set which may be stored may be provided by a firstparty, a second data set which may be stored may be provided by anunrelated second party, and yet a third data set which may be stored,may be provided by an third party unrelated to the first and secondparty. Each of these three exemplary data sets may contain differentinformation that is stored using different data storage formats and/ortechniques. Further, each data set may contain subsets of data that alsomay be distinct from other subsets.

As stated above, in various embodiments of system, the data can bestored without regard to a common format. However, in one exemplaryembodiment of the invention, the data set (e.g., BLOB) may be annotatedin a standard manner when provided for manipulating the data onto thefinancial transaction instrument. The annotation may comprise a shortheader, trailer, or other appropriate indicator related to each data setthat is configured to convey information useful in managing the variousdata sets. For example, the annotation may be called a “conditionheader”, “header”, “trailer”, or “status”, herein, and may comprise anindication of the status of the data set or may include an identifiercorrelated to a specific issuer or owner of the data. In one example,the first three bytes of each data set BLOB may be configured orconfigurable to indicate the status of that particular data set; e.g.,LOADED, INITIALIZED, READY, BLOCKED, REMOVABLE, or DELETED. Subsequentbytes of data may be used to indicate for example, the identity of theissuer, user, transaction/membership account identifier or the like.Each of these condition annotations are further discussed herein.

The data set annotation may also be used for other types of statusinformation as well as various other purposes. For example, the data setannotation may include security information establishing access levels.The access levels may, for example, be configured to permit only certainindividuals, levels of employees, companies, or other entities to accessdata sets, or to permit access to specific data sets based on thetransaction, merchant, issuer, user or the like. Furthermore, thesecurity information may restrict/permit only certain actions such asaccessing, modifying, and/or deleting data sets. In one example, thedata set annotation indicates that only the data set owner or the userare permitted to delete a data set, various identified users may bepermitted to access the data set for reading, and others are altogetherexcluded from accessing the data set. However, other access restrictionparameters may also be used allowing various entities to access a dataset with various permission levels as appropriate.

The data, including the header or trailer may be received by astand-alone interaction device configured to add, delete, modify, oraugment the data in accordance with the header or trailer. As such, inone embodiment, the header or trailer is not stored on the transactiondevice along with the associated issuer-owned data but instead theappropriate action may be taken by providing to the transactioninstrument user at the stand-alone device, the appropriate option forthe action to be taken. The present invention contemplates a datastorage arrangement wherein the header or trailer, or header or trailerhistory, of the data is stored on the transaction instrument in relationto the appropriate data.

One skilled in the art will also appreciate that, for security reasons,any databases, systems, devices, servers or other components of thepresent invention may consist of any combination thereof at a singlelocation or at multiple locations, wherein each database or systemincludes any of various suitable security features, such as firewalls,access codes, encryption, decryption, compression, decompression, and/orthe like.

The various system components discussed herein may include one or moreof the following: a host server or other computing systems including aprocessor for processing digital data; a memory coupled to the processorfor storing digital data; an input digitizer coupled to the processorfor inputting digital data; an application program stored in the memoryand accessible by the processor for directing processing of digital databy the processor; a display device coupled to the processor and memoryfor displaying information derived from digital data processed by theprocessor; and a plurality of databases. Various databases used hereinmay include: client data; merchant data; financial institution data;and/or like data useful in the operation of the present invention. Asthose skilled in the art will appreciate, user computer may include anoperating system (e.g., Windows NT, 95/98/2000, OS2, UNIX, Linux,Solaris, MacOS, etc.) as well as various conventional support softwareand drivers typically associated with computers. The computer mayinclude any suitable personal computer, network computer, workstation,minicomputer, mainframe or the like. User computer can be in a home orbusiness environment with access to a network. In an exemplaryembodiment, access is through a network or the Internet through acommercially-available web-browser software package.

As used herein, the term “network” (or similar terms) shall include anyelectronic communications means which incorporates both hardware andsoftware components of such. Communication among the parties inaccordance with the present invention may be accomplished through anysuitable communication channels, such as, for example, a telephonenetwork, an extranet, an intranet, Internet, point of interaction device(point of sale device, personal digital assistant, cellular phone,kiosk, etc.), online communications, satellite communications, off-linecommunications, wireless communications, transponder communications,local area network (LAN), wide area network (WAN), networked or linkeddevices, keyboard, mouse and/or any suitable communication or data inputmodality. Moreover, although the invention is frequently describedherein as being implemented with TCP/IP communications protocols, theinvention may also be implemented using IPX, Appletalk, IP-6, NetBIOS,OSI or any number of existing or future protocols. If the network is inthe nature of a public network, such as the Internet, it may beadvantageous to presume the network to be insecure and open toeavesdroppers. Specific information related to the protocols, standards,and application software utilized in connection with the Internet isgenerally known to those skilled in the art and, as such, need not bedetailed herein. See, for example, Dilip Naik, Internet Standards AndProtocols (1998); Java 2 Complete, various authors, (Sybex 1999);Deborah Ray And Eric Ray, Mastering Html 4.0 (1997); And Loshin, Tcp/IpClearly Explained (1997) and David Gourley and Brian Totty, Http, TheDefinitive Guide (2002), the contents of which are hereby incorporatedby reference.

The invention may be described herein in terms of functional blockcomponents, screen shots, optional selections and various processingsteps. It should be appreciated that such functional blocks may berealized by any number of hardware and/or software components configuredto perform the specified functions. For example, the present inventionmay employ various integrated circuit components, e.g., memory elements,processing elements, logic elements, look-up tables, and/or the like,which may carry out a variety of functions under the control of one ormore microprocessors or other control devices. Similarly, the softwareelements of system may be implemented with any programming or scriptinglanguage such as C, C++, Java, COBOL, assembler, PERL, Visual Basic, SQLStored Procedures, extensible markup language (XML), with the variousalgorithms being implemented with any combination of data structures,objects, processes, routines or other programming elements. Further, itshould be noted that the present invention may employ any number ofconventional techniques for data transmission, signaling, dataprocessing, network control, and/or the like. Still further, a systemmay be used to detect or prevent security issues with a client-sidescripting language, such as JavaScript, VBScript or the like. For abasic introduction of cryptography and network security, see any of thefollowing references: (1) “Applied Cryptography: Protocols, Algorithms,And Source Code In C,” by Bruce Schreier, published by John Wiley & Sons(second edition, 1995); (2) “Java Cryptography” by Jonathan Knudson,published by O'Reilly & Associates (1998); (3) “Cryptography & NetworkSecurity: Principles & Practice” by William Stallings, published byPrentice Hall; all of which are hereby incorporated by reference.

As will be appreciated by one of ordinary skill in the art, the systemmay be embodied as a customization of an existing system, an add-onproduct, upgraded software, a stand alone system, a distributed system,a method, a data processing system, a device for data processing, and/ora computer program product. Accordingly, the present invention may takethe form of an entirely software embodiment, an entirely hardwareembodiment, or an embodiment combining aspects of both software andhardware. Furthermore, the present invention may take the form of acomputer program product on a computer-readable storage medium havingcomputer-readable program code means embodied in the storage medium. Anysuitable computer-readable storage medium may be utilized, includinghard disks, CD-ROM, optical storage devices, magnetic storage devices,and/or the like.

These software elements may be loaded onto a general purpose computer,special purpose computer, or other programmable data processingapparatus to produce a machine, such that the instructions that executeon the computer or other programmable data processing apparatus createmeans for implementing the functions specified in the flowchart block orblocks. These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the function specified in the flowchart block or blocks.The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer-implemented process such that theinstructions which execute on the computer or other programmableapparatus provide steps for implementing the functions specified in theflowchart block or blocks.

Accordingly, functional blocks of the block diagrams and flowchartillustrations support combinations of means for performing the specifiedfunctions, combinations of steps for performing the specified functions,and program instruction means for performing the specified functions. Itwill also be understood that each functional block of the block diagramsand flowchart illustrations, and combinations of functional blocks inthe block diagrams and flowchart illustrations, can be implemented byeither special purpose hardware-based computer systems which perform thespecified functions or steps, or suitable combinations of specialpurpose hardware and computer instructions. Further, illustrations ofthe process flows and the descriptions thereof may make reference touser windows, web pages, web sites, web forms, prompts, etc.Practitioners will appreciate that the illustrated steps describedherein may comprise in any number of configurations including the use ofwindows, web pages, web forms, popup windows, prompts and/or the like.It should be further appreciated that the multiple steps as illustratedand described may be combined into single web pages and/or windows buthave been expanded for the sake of simplicity. In other cases, stepsillustrated and described as single process steps may be separated intomultiple web pages and/or windows but have been combined for simplicity.

Practitioners will appreciate that there are a number of methods fordisplaying data within a browser-based document. Data may be representedas standard text or within a fixed list, scrollable list, drop-downlist, editable text field, fixed text field, pop-up window, and/or thelike. Likewise, there are a number of methods available for modifyingdata in a web page such as, for example, free text entry using akeyboard, selection of menu items, check boxes, option boxes,

While the steps outlined above represent a specific embodiment of theinvention, practitioners will appreciate that there are any number ofcomputing algorithms and user interfaces that may be applied to createsimilar results. The steps are presented for the sake of explanationonly and are not intended to limit the scope of the invention in anyway.

Benefits, other advantages, and solutions to problems have beendescribed herein with regard to specific embodiments. However, thebenefits, advantages, solutions to problems, and any element(s) that maycause any benefit, advantage, or solution to occur or become morepronounced are not to be construed as critical, required, or essentialfeatures or elements of any or all the claims or the invention. Itshould be understood that the detailed description and specificexamples, indicating exemplary embodiments of the invention, are givenfor purposes of illustration only and not as limitations. Many changesand modifications within the scope of the instant invention may be madewithout departing from the spirit thereof, and the invention includesall such modifications. Corresponding structures, materials, acts, andequivalents of all elements in the claims below are intended to includeany structure, material, or acts for performing the functions incombination with other claim elements as specifically claimed. The scopeof the invention should be determined by the appended claims and theirlegal equivalents, rather than by the examples given above. Reference toan element in the singular is not intended to mean “one and only one”unless explicitly so stated, but rather “one or more.” Moreover, where aphrase similar to ‘at least one of A, B, and C’ is used in the claims,it is intended that the phrase be interpreted to mean that A alone maybe present in an embodiment, B alone may be present in an embodiment, Calone may be present in an embodiment, or that any combination of theelements A, B and C may be present in a single embodiment; for example,A and B, A and C, B and C, or A and B and C. Although the invention hasbeen described as a method, it is contemplated that it may be embodiedas computer program instructions on a tangible computer-readablecarrier, such as a magnetic or optical memory or a magnetic or opticaldisk. All structural, chemical, and functional equivalents to theelements of the above-described exemplary embodiments that are known tothose of ordinary skill in the art are expressly incorporated herein byreference and are intended to be encompassed by the present claims.Moreover, it is not necessary for a device or method to address each andevery problem sought to be solved by the present invention, for it to beencompassed by the present claims. Furthermore, no element, component,or method step in the present disclosure is intended to be dedicated tothe public regardless of whether the element, component, or method stepis explicitly recited in the claims. No claim element herein is to beconstrued under the provisions of 35 U.S.C. 112, sixth paragraph, unlessthe element is expressly recited using the phrase “means for.” As usedherein, the terms “comprises”, “comprising”, or any other variationthereof, are intended to cover a non-exclusive inclusion, such that aprocess, method, article, or apparatus that comprises a list of elementsdoes not include only those elements but may include other elements notexpressly listed or inherent to such process, method, article, orapparatus.

1. A method comprising: calculating, by a risk analysis computer, acomprehensive consumer default risk value for a consumer based uponconsumer credit data, internal data associated with said consumercomprising consumer transactional data, and estimated spend capacity,wherein said comprehensive consumer default risk value represents a riskassociated with said consumer defaulting on an existing debt obligation;and wherein said calculating comprises: assigning, by said risk analysiscomputer, said consumer to a consumer population segment based uponprimary residence value; selecting, by said risk analysis computer, anappropriate risk factor relationship based upon said consumer creditdata and said internal data.
 2. The method of claim 1, furthercomprising determining a strategy to interact with said consumer basedon said comprehensive consumer default risk value for said consumer. 3.The method of claim 2, wherein said determining a strategy furthercomprises making credit approval decisions for said consumer based uponsaid comprehensive consumer default risk value.
 4. The method of claim2, wherein said determining a strategy further comprises soliciting saidconsumer for additional products in accordance with said comprehensiveconsumer default risk value.
 5. The method of claim 1, furthercomprising establishing a new transaction account based upon saidcomprehensive consumer default risk value.
 6. The method of claim 1,further comprising establishing a new mortgage based upon saidcomprehensive consumer default risk value.
 7. The method of claim 1,further comprising establishing a new automobile loan based upon saidcomprehensive consumer default risk value.
 8. The method of claim 1,further comprising establishing a new student loan based upon saidcomprehensive consumer default risk value.
 9. The method of claim 2,wherein said determining a strategy further comprises discontinuing arelationship with said consumer based upon said comprehensive consumerdefault risk value.
 10. The method of claim 1, further comprisingmodeling, by said risk analysis computer, said consumer spendingpatterns of said consumer using said credit data associated with saidconsumer to obtain said estimated spend capacity of said consumer. 11.The method of claim 10, wherein said modeling consumer spending patternsfurther comprises: receiving credit bureau data comprising a pluralityof accounts of said consumer over a previous period of time; identifyingany balance transfers into at least one of said plurality of accountsbased upon said credit bureau data; discounting any spending identifiedfor any of said plurality of accounts for any portion of said previousperiod of time in which a balance transfer to such account isidentified; and estimating a purchasing ability of said consumer basedon said credit bureau data and said discounting.
 12. The method of claim10, wherein said modeling consumer spending patterns further comprises:identifying at least two categories wherein a first category includesconsumers that primarily pay down credit account balances and a secondcategory including consumers that primarily revolve credit accountbalances; and assigning said consumer to at least one of saidcategories.
 13. The method of claim 10, further comprising determining astrategy to interact with said consumer based on said comprehensiveconsumer default risk value for said consumer.
 14. The method of claim13, wherein said determining a strategy further comprises at least oneof making credit approval decisions for said consumer based upon saidcomprehensive consumer default risk value, and soliciting said consumerfor products in accordance with said comprehensive consumer default riskvalue.
 15. The method of claim 11, further comprising establishing a newtransaction account based upon said comprehensive consumer default riskvalue.
 16. The method of claim 11, further comprising establishing a newmortgage based upon said comprehensive consumer default risk value. 17.The method of claim 11, further comprising establishing at least one ofa new automobile loan and a new student loan based upon saidcomprehensive consumer default risk value.
 18. A system comprising: anon-transitory memory communicating with a risk analysis processor; saidnon-transitory memory having instructions stored thereon that, inresponse to execution by said processor, cause said processor to performoperations comprising: calculating, by said risk analysis processor, acomprehensive consumer default risk value for a consumer based uponconsumer credit data, internal data associated with said consumercomprising consumer transactional data, and estimated spend capacity,wherein said comprehensive consumer default risk value represents a riskassociated with said consumer defaulting on an existing debt obligation;and wherein said calculating comprises: assigning, by said risk analysisprocessor, said consumer to a consumer population segment based uponprimary residence value; selecting, by said risk analysis processor, anappropriate risk factor relationship based upon said consumer creditdata and said internal data.
 19. An article of manufacture including anon-transitory computer readable medium having instructions storedthereon that, in response to execution by a risk analysis computingdevice, cause said computing device to perform operations comprising:calculating, by said risk analysis computing device, a comprehensiveconsumer default risk value for a consumer based upon consumer creditdata, internal data associated with said consumer comprising consumertransactional data, and estimated spend capacity, wherein saidcomprehensive consumer default risk value represents a risk associatedwith said consumer defaulting on an existing debt obligation; andwherein said calculating comprises: assigning, by said risk analysiscomputing device, said consumer to a consumer population segment basedupon primary residence value; selecting, by said risk analysis computingdevice, an appropriate risk factor relationship based upon said consumercredit data and said internal data.